{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T04:50:26Z","timestamp":1771303826704,"version":"3.50.1"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030766566","type":"print"},{"value":"9783030766573","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-76657-3_5","type":"book-chapter","created":{"date-parts":[[2021,5,15]],"date-time":"2021-05-15T20:02:23Z","timestamp":1621108943000},"page":"79-92","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Combining Deep Learning and Mathematical Morphology for Historical Map Segmentation"],"prefix":"10.1007","author":[{"given":"Yizi","family":"Chen","sequence":"first","affiliation":[]},{"given":"Edwin","family":"Carlinet","sequence":"additional","affiliation":[]},{"given":"Joseph","family":"Chazalon","sequence":"additional","affiliation":[]},{"given":"Cl\u00e9ment","family":"Mallet","sequence":"additional","affiliation":[]},{"given":"Bertrand","family":"Dum\u00e9nieu","sequence":"additional","affiliation":[]},{"given":"Julien","family":"Perret","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,5,16]]},"reference":[{"key":"5_CR1","unstructured":"Angulo, J., Serra, J.: Mathematical morphology in color spaces applied to the analysis of cartographic images. In: Proceedings of GEOPRO, vol. 3, pp. 59\u201366 (2003)"},{"issue":"5","key":"5_CR2","doi-asserted-by":"publisher","first-page":"898","DOI":"10.1109\/TPAMI.2010.161","volume":"33","author":"P Arbelaez","year":"2010","unstructured":"Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intel. 33(5), 898\u2013916 (2010)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intel."},{"issue":"12","key":"5_CR3","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Analy. Mach. Intell. 39(12), 2481\u20132495 (2017)","journal-title":"IEEE Trans. Pattern Analy. Mach. Intell."},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Bai, M., Urtasun, R.: Deep watershed transform for instance segmentation. In: Proceedings of Conference on Computer Vision and Pattern Recognition, pp. 5221\u20135229 (2017)","DOI":"10.1109\/CVPR.2017.305"},{"key":"5_CR5","doi-asserted-by":"crossref","unstructured":"Barcelos, I.B., et al.: Exploring hierarchy simplification for non-significant region removal. In: SIBGRAPI Conference on Graphics, Patterns and Images, pp. 100\u2013107 (2019)","DOI":"10.1109\/SIBGRAPI.2019.00022"},{"key":"5_CR6","first-page":"69","volume-title":"Mathematical Morphology (ISMM)","author":"S Beucher","year":"1994","unstructured":"Beucher, S.: Watershed, hierarchical segmentation and waterfall algorithm. In: Serra, J., Soille, P. (eds.) Mathematical Morphology (ISMM), pp. 69\u201376. Springer, Dordrecht (1994)"},{"key":"5_CR7","doi-asserted-by":"crossref","unstructured":"Chen, K., et al.: Hybrid task cascade for instance segmentation. In: Proceedings of Conference on Computer Vision and Pattern Recognition, pp. 4974\u20134983 (2019)","DOI":"10.1109\/CVPR.2019.00511"},{"key":"5_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/978-3-642-36824-0_3","volume-title":"Graphics Recognition. New Trends and Challenges","author":"Y-Y Chiang","year":"2013","unstructured":"Chiang, Y.-Y., Leyk, S., Knoblock, C.A.: Efficient and robust graphics recognition from historical maps. In: Kwon, Y.-B., Ogier, J.-M. (eds.) GREC 2011. LNCS, vol. 7423, pp. 25\u201335. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-36824-0_3"},{"key":"5_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1007\/978-3-030-20351-1_2","volume-title":"Information Processing in Medical Imaging","author":"JR Clough","year":"2019","unstructured":"Clough, J.R., Oksuz, I., Byrne, N., Schnabel, J.A., King, A.P.: Explicit topological priors for deep-learning based image segmentation using persistent homology. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 16\u201328. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20351-1_2"},{"issue":"2\u20133","key":"5_CR10","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/s10851-005-4892-4","volume":"22","author":"M Couprie","year":"2005","unstructured":"Couprie, M., Najman, L., Bertrand, G.: Quasi-linear algorithms for the topological watershed. J. Math. Imaging Vis. 22(2\u20133), 231\u2013249 (2005)","journal-title":"J. Math. Imaging Vis."},{"issue":"5","key":"5_CR11","doi-asserted-by":"publisher","first-page":"925","DOI":"10.1109\/TPAMI.2009.71","volume":"32","author":"J Cousty","year":"2009","unstructured":"Cousty, J., Bertrand, G., Najman, L., Couprie, M.: Watershed cuts: thinnings, shortest path forests, and topological watersheds. IEEE Trans. Pattern Anal. Mach. Intell. 32(5), 925\u2013939 (2009)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"5_CR12","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1080\/13658810410001713407","volume":"19","author":"C Dietzel","year":"2005","unstructured":"Dietzel, C., Herold, M., Hemphill, J.J., Clarke, K.C.: Spatio-temporal dynamics in California\u2019s central valley: empirical links to urban theory. Int. J. Geogr. Inf. Sci. (IJGIS) 19(2), 175\u2013195 (2005)","journal-title":"Int. J. Geogr. Inf. Sci. (IJGIS)"},{"issue":"4","key":"5_CR13","doi-asserted-by":"publisher","first-page":"837","DOI":"10.1109\/TPAMI.2018.2890586","volume":"42","author":"J Favreau","year":"2020","unstructured":"Favreau, J., Lafarge, F., Bousseau, A., Auvolat, A.: Extracting geometric structures in images with delaunay point processes. IEEE Trans. Pattern Anal. Mach. Intell. 42(4), 837\u2013850 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"4","key":"5_CR14","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1016\/j.imavis.2008.06.012","volume":"27","author":"A Hanbury","year":"2009","unstructured":"Hanbury, A., Marcotegui, B.: Morphological segmentation on learned boundaries. Image Vis. Comput. 27(4), 480\u2013488 (2009)","journal-title":"Image Vis. Comput."},{"key":"5_CR15","doi-asserted-by":"crossref","unstructured":"He, J., Zhang, S., Yang, M., Shan, Y., Huang, T.: BDCN: bi-directional cascade network for perceptual edge detection. IEEE Trans. Pattern Anal. Mach. Intell. (2020)","DOI":"10.1109\/CVPR.2019.00395"},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"Kirillov, A., He, K., Girshick, R., Rother, C., Doll\u00e1r, P.: Panoptic segmentation. In: Proceedings of Conference on Computer Vision and Pattern Recognition, pp. 9404\u20139413 (2019)","DOI":"10.1109\/CVPR.2019.00963"},{"issue":"5","key":"5_CR17","doi-asserted-by":"publisher","first-page":"953","DOI":"10.1016\/j.patcog.2005.10.018","volume":"39","author":"S Leyk","year":"2006","unstructured":"Leyk, S., Boesch, R., Weibel, R.: Saliency and semantic processing: extracting forest cover from historical topographic maps. Pattern Recogn. 39(5), 953\u2013968 (2006)","journal-title":"Pattern Recogn."},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"5_CR19","doi-asserted-by":"crossref","unstructured":"Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of International Conference of Computer Vision (ICCV), vol. 2, pp. 416\u2013423 (2001)","DOI":"10.1109\/ICCV.2001.937655"},{"issue":"1","key":"5_CR20","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/0165-1684(94)90060-4","volume":"38","author":"F Meyer","year":"1994","unstructured":"Meyer, F.: Topographic distance and watershed lines. Signal Process. 38(1), 113\u2013125 (1994)","journal-title":"Signal Process."},{"issue":"3","key":"5_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1360612.1360691","volume":"27","author":"A Orzan","year":"2008","unstructured":"Orzan, A., Bousseau, A., Winnem\u00f6ller, H., Barla, P., Thollot, J., Salesin, D.: Diffusion curves: a vector representation for smooth-shaded images. ACM Trans. Graph. 27(3), 1\u20138 (2008)","journal-title":"ACM Trans. Graph."},{"issue":"4","key":"5_CR22","doi-asserted-by":"publisher","first-page":"1676","DOI":"10.1109\/TIP.2017.2779604","volume":"27","author":"B Perret","year":"2017","unstructured":"Perret, B., Cousty, J., Guimaraes, S.J.F., Maia, D.S.: Evaluation of hierarchical watersheds. IEEE Trans. Image Process. 27(4), 1676\u20131688 (2017)","journal-title":"IEEE Trans. Image Process."},{"key":"5_CR23","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1016\/j.patrec.2019.10.008","volume":"128","author":"B Perret","year":"2019","unstructured":"Perret, B., Cousty, J., Guimar\u00e3es, S.J.F., Kenmochi, Y., Najman, L.: Removing non-significant regions in hierarchical clustering and segmentation. Pattern Recogn. Lett. 128, 433\u2013439 (2019)","journal-title":"Pattern Recogn. Lett."},{"issue":"1","key":"5_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2015.48","volume":"2","author":"J Perret","year":"2015","unstructured":"Perret, J., Gribaudi, M., Barthelemy, M.: Roads and cities of 18th century France. Sci. Data 2(1), 1\u20137 (2015)","journal-title":"Sci. Data"},{"key":"5_CR25","unstructured":"Pr\u00e9fecture de la Seine, service du Plan: Atlas des vingt arrondissements de Paris [1 vol. (3 pl., 16 pl. doubles), 68 cm]. Paris. L. Wuhrer. ARK: 73873\/pf0000935524 (1925), Biblioth\u00e8que de l\u2019H\u00f4tel de Ville, Ville de Paris, Paris"},{"key":"5_CR26","doi-asserted-by":"crossref","unstructured":"Roerdink, J.B., Meijster, A.: The watershed transform: definitions, algorithms and parallelization strategies. Fundam. Informaticae 41(1, 2), 187\u2013228 (2000)","DOI":"10.3233\/FI-2000-411207"},{"key":"5_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1007\/978-3-319-46466-4_19","volume-title":"Computer Vision \u2013 ECCV 2016","author":"B Romera-Paredes","year":"2016","unstructured":"Romera-Paredes, B., Torr, P.H.S.: Recurrent instance segmentation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 312\u2013329. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46466-4_19"},{"key":"5_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"issue":"8","key":"5_CR29","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1109\/83.403422","volume":"4","author":"P Salembier","year":"1995","unstructured":"Salembier, P., Serra, J.: Flat zones filtering, connected operators, and filters by reconstruction. IEEE Trans. Image Process. 4(8), 1153\u20131160 (1995)","journal-title":"IEEE Trans. Image Process."},{"key":"5_CR30","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"5_CR31","volume-title":"Morphological Image Analysis: Principles and Applications","author":"P Soille","year":"2013","unstructured":"Soille, P.: Morphological Image Analysis: Principles and Applications. Springer, Heidelberg (2013)"},{"key":"5_CR32","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.neucom.2019.09.083","volume":"376","author":"L Xie","year":"2020","unstructured":"Xie, L., Qi, J., Pan, L., Wali, S.: Integrating deep convolutional neural networks with marker-controlled watershed for overlapping nuclei segmentation in histopathology images. Neurocomputing 376, 166\u2013179 (2020)","journal-title":"Neurocomputing"},{"key":"5_CR33","doi-asserted-by":"crossref","unstructured":"Xie, S., Tu, Z.: Holistically-nested edge detection. In: Proceedings of Conference on Computer Vision and Pattern Recognition, pp. 1395\u20131403 (2015)","DOI":"10.1109\/ICCV.2015.164"},{"key":"5_CR34","doi-asserted-by":"crossref","unstructured":"Zhang, Z., et al.: Superedge grouping for object localization by combining appearance and shape informations. In: Proceedings of Conference on Computer Vision and Pattern Recognition, pp. 3266\u20133273 (2012)","DOI":"10.1109\/CVPR.2012.6248063"},{"issue":"1\u20132","key":"5_CR35","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/s11263-005-4638-1","volume":"62","author":"SC Zhu","year":"2005","unstructured":"Zhu, S.C., Guo, C.E., Wang, Y., Xu, Z.: What are textons? Intl. J. Comput. Vis. 62(1\u20132), 121\u2013143 (2005)","journal-title":"Intl. J. Comput. Vis."}],"container-title":["Lecture Notes in Computer Science","Discrete Geometry and Mathematical Morphology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-76657-3_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T22:03:02Z","timestamp":1747260182000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-76657-3_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030766566","9783030766573"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-76657-3_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"16 May 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DGMM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Discrete Geometry and Mathematical Morphology","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Uppsala","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sweden","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 May 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 May 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dgmm2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dgmm2021.se\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"59","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"36","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"61% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1,3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}